Accurate prediction of virulence factors using pre-train protein language model and ensemble learning

As bacterial pathogens develop increasing resistance to antibiotics, strategies targeting virulence factors (VFs) have emerged as a promising and effective approach for treating bacterial infections. Existing methods mainly relied on sequence similarity, and remote homology relationships cannot be discovered by sequence analysis alone.

To address this limitation, we developed a protein language model and ensemble learning approach for VF identification (PLMVF). Specifically, we extracted features from protein sequences using ESM-2 and their three-dimensional (3D) structures using ESMFold. We calculated the true TM-score of the proteins based on their 3D structures and trained a TM-predictor model to predict structural similarity, thereby capturing hidden remote homology information within the sequences. Subsequently, we concatenated the sequence-level features extracted by ESM-2 with the predicted TM-score features to form a comprehensive feature set for prediction. Extensive experimental validation demonstrated that PLMVF achieved an accuracy (ACC) of 86.1%, significantly outperforming existing models across multiple evaluation metrics. This study provided an ideal tool for identifying novel targets in the development of anti-virulence therapies, offering promise for the effective prevention and control of pathogenic bacterial infections.

The proposed PLMVF model offers an efficient computational approach for VF identification.